@inproceedings{niculae-etal-2017-argument,
title = "Argument Mining with Structured {SVM}s and {RNN}s",
author = "Niculae, Vlad and
Park, Joonsuk and
Cardie, Claire",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1091",
doi = "10.18653/v1/P17-1091",
pages = "985--995",
abstract = "We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20{\%} of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.",
}
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%0 Conference Proceedings
%T Argument Mining with Structured SVMs and RNNs
%A Niculae, Vlad
%A Park, Joonsuk
%A Cardie, Claire
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F niculae-etal-2017-argument
%X We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.
%R 10.18653/v1/P17-1091
%U https://aclanthology.org/P17-1091
%U https://doi.org/10.18653/v1/P17-1091
%P 985-995
Markdown (Informal)
[Argument Mining with Structured SVMs and RNNs](https://aclanthology.org/P17-1091) (Niculae et al., ACL 2017)
ACL
- Vlad Niculae, Joonsuk Park, and Claire Cardie. 2017. Argument Mining with Structured SVMs and RNNs. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 985–995, Vancouver, Canada. Association for Computational Linguistics.